On-animal motion sensing using accelerometers as a tool for monitoring sheep behaviour and health status Jamie Barwick Bachelor of Rural Science (Honours) University of New England A thesis submitted for the degree of Doctor of Philosophy in the School of Science of Technology, University of New England December, 2016 i Declaration I certify that the substance of this thesis has not already been submitted for any degree and is not currently being submitted for any other degree or qualification. I certify that any help received in preparing this thesis, and all sources used, have been acknowledged in this thesis. Jamie DJ Barwick i Acknowledgements I wish to sincerely thank Associate Professor Mark Trotter. You and your family’s support and friendship throughout the past 3 years has been tremendous and is greatly appreciated. Your knowledge of the livestock and precision agriculture industries has proved invaluable. I am indebted to you for your assistance and truly grateful for all the time you have dedicated to me ensuring this project was completed. To Dr Robin Dobos, your enthusiasm, advice and encouragement has been essential over the past 3 years, as has your patience. Your diligence and feedback has certainly been appreciated. I am truly grateful for all the time you have dedicated to me. I wish to express my sincere thanks to my principal supervisor, Professor David Lamb. I am grateful for your supervision and advice throughout the course of this thesis, especially throughout the final months. To Mr Derek Schneider, thank you for all your assistance with data collection, analysis and presentation. The friendship of you and your family is sincerely appreciated. Thank you to Dr Mitchell Welch for all your assistance with data analysis. Your assistance has proved invaluable. Thank you to Dr Amanda Doughty for letting me be involved with your research projects. Your assistance was greatly appreciated. Thank you to the funding bodies: the Sheep Cooperative Research Centre, the School of Science and Technology and the Commonwealth for their generous scholarships and commitment to postgraduate research and training. Finally I would like to thank my family, Mum, Dad and Mathew who have encouraged me to take the opportunities which have presented themselves. The numerous sacrifices you have made for me are truly appreciated. ii Summary An opportunity exists to infer the physiological and physical state of an animal from changes in their behaviour. As resting, eating, walking and ruminating are the predominant daily activities of ruminant animals, monitoring these behaviours could provide valuable information for monitoring individual animal health and welfare status. Conventional animal monitoring methods have relied on visual observations of animals by human labour. This can only provide information on an animal’s behaviour for the period in which they are being observed. Historically, observations could be made for long periods where shepherds were employed to observe their flocks nearly constantly. This is obviously no-longer feasible in the current livestock industry. Recently, with the advent of small, low power accelerometer technology, the ability to remotely monitor animal movement continuously has arisen. This is achieved through the application of on-animal inertia monitoring unit (IMU) sensors. This movement data might potentially lead to continuous behavioural monitoring of livestock. These devices have been developed for higher value livestock such as dairy cattle but little research or development has been directed towards their use in sheep. Previous work has evaluated collar and leg deployments however the sheep industry demands these devices be in an eartag form factor to align with current industry practices. Therefore, this thesis aims to evaluate the potential for using ear-borne accelerometer devices to detect and categorise key behaviours expressed by sheep. Deviation from normal patterns of behaviour may be used as an indicator of changes in individual health status. If behaviour can be categorised using the data collected by these body worn devices and radio telemetry incorporated, animal health could be monitored in near real time allowing early treatment intervention when necessary, ultimately improving on- farm productivity. Scoping work in this thesis identified the difference in acceleration signals between the basic sheep behaviours: grazing, walking and resting, giving potential for discrimination between behaviours with classification algorithms. Subsequently a successful behaviour classification algorithm was developed based on accelerometer iii data obtained from the ear deployment, yielding activity predictions similar to those obtained through visual observation. To apply this technology to a commercial application, a simulated lameness experiment was designed, where lame walking behaviour was discriminated from sound walking events successfully using the ear and leg modes of deployment. The final experiment investigated the application of ear deployed accelerometer devices to detect behavioural changes associated with increased infection by internal parasites, a disease of extreme economic importance within Australia. Animals with a higher faecal worm egg count were shown to have a lower probability of engaging in longer periods of activity, however this experiment was limited by a very mild level of infection. Overall this thesis demonstrates that sheep behaviour can be classified using an ear-mounted tri-axial accelerometer sensor, the first of its kind to date. It also explored the suitability of using time-series behavioural classification data as an early indicator of health and welfare issues. This work aims to link a previous “research only” technology in sheep, to a commercial application, a stepping stone towards bridging the gap between research and industry adoption. iv Table of contents Declaration ____________________________________________________________________________________ i Acknowledgements ___________________________________________________________________________ ii Summary _____________________________________________________________________________________ iii Table of contents _____________________________________________________________________________ v List of acronyms ____________________________________________________________________________ viii _____________________________________________________________________________________ 1 General introduction _____________________________________________________________________ 1 _____________________________________________________________________________________ 4 Review of literature & objectives of thesis ____________________________________________ 4 2.1 Sheep behaviour _____________________________________________________________________ 4 2.2 Using behaviour to predict disease _______________________________________________ 14 2.3 Sheep diseases _____________________________________________________________________ 17 2.4 Conventional animal behaviour monitoring _____________________________________ 25 2.5 Remote animal behaviour monitoring ___________________________________________ 30 2.6 Accelerometer based activity monitoring ________________________________________ 37 2.7 Commercially available accelerometers for cattle _______________________________ 61 2.8 Scope of thesis _____________________________________________________________________ 65 ___________________________________________________________________________________ 67 Methodology for using tri-axial accelerometers for behaviour monitoring ____ 67 3.1 Introduction _______________________________________________________________________ 67 3.2 Accelerometer specifications _____________________________________________________ 67 3.3 Accelerometer attachment on experimental sheep _____________________________ 72 3.4 Accelerometer sample rate _______________________________________________________ 77 3.5 Behaviour classification ___________________________________________________________ 77 v 3.6 Accelerometer signal annotation _________________________________________________ 78 3.7 Data processing and analysis _____________________________________________________ 83 ___________________________________________________________________________________ 94 Interpreting tri-axial acceleration signals __________________________________________ 94 4.1 Research objectives _______________________________________________________________ 94 4.2 Materials and methods ____________________________________________________________ 95 4.3 Resting _____________________________________________________________________________ 95 4.4 Walking ___________________________________________________________________________ 101 4.5 Grazing ____________________________________________________________________________ 108 4.6 Conclusion ________________________________________________________________________ 112 _________________________________________________________________________________ 113 Post-event classification of basic sheep movement from a tri-axial accelerometer _________________________________________________________________________ 113 5.1 Introduction ______________________________________________________________________ 113 5.2 Research objectives ______________________________________________________________ 113 Part A – Proof of concept _____________________________________________________________ 114 Part B – Testing the transferability of metrics